Autoencoder neural network raspberry pi

gistfile1.txt

//Autoencoder learning test with raspicam
//press <Y> at start It's tested with 0..9 28x28 pixel digits pattern mnist.png filname 289x289 orginal
//orginal image taken from
//       https://blog.webkid.io/datasets-for-machine-learning/

//#define USE_RASPICAM_INPUT //If you want to use raspicam input data


#include <opencv2/highgui/highgui.hpp>  // OpenCV window I/O
#include <opencv2/imgproc/imgproc.hpp> // Gaussian Blur
#include <stdio.h>
#include <raspicam/raspicam_cv.h>
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>        // Basic OpenCV structures (cv::Mat, Scalar)
#include <cstdlib>
#include <ctime>
#include <math.h>  // exp
#include <stdlib.h>// exit(0);
//#include <iostream>
    char filename[100];
    char filename2[100];
    char filename_dst[100];
using namespace std;
using namespace cv;
//const float LearningRate =0.05;
//const float Momentum = 0.025;
const float LearningRate =0.02;
const float Momentum = 0.04;
 //  float noise_percent =50.0f;
   float noise_percent =30.0f;

void sigmoid_mat(Mat image)
{
	float* ptr_src_index;
	ptr_src_index = image.ptr<float>(0);
    int nRows = image.rows;
    int nCols = image.cols;
    for(int i=0;i<nRows;i++)
    {
		for(int j=0;j<nCols;j++)
		{
			*ptr_src_index = 1.0/(1.0 + exp(-(*ptr_src_index)));//Sigmoid function
			ptr_src_index++;
		}
	}
}
#include <termios.h>
#include <unistd.h>
#include <fcntl.h>

int kbhit(void)
{
  struct termios oldt, newt;
  int ch;
  int oldf;

  tcgetattr(STDIN_FILENO, &oldt);
  newt = oldt;
  newt.c_lflag &= ~(ICANON | ECHO);
  tcsetattr(STDIN_FILENO, TCSANOW, &newt);
  oldf = fcntl(STDIN_FILENO, F_GETFL, 0);
  fcntl(STDIN_FILENO, F_SETFL, oldf | O_NONBLOCK);

  ch = getchar();

  tcsetattr(STDIN_FILENO, TCSANOW, &oldt);
  fcntl(STDIN_FILENO, F_SETFL, oldf);

  if(ch != EOF)
  {
    ungetc(ch, stdin);
    return 1;
  }

  return 0;
}

void local_normalizing(Mat gray)
{

	#define BLUR_FLT_NUMERATOR 2
	#define BLUR_FLT_DENOMINATOR 20

    Mat float_gray, blur, num, den, store_gray;
	store_gray = gray;//Initialize size
    // convert to floating-point image
    gray.convertTo(float_gray, CV_32F, 1.0/255.0);

    // numerator = img - gauss_blur(img)
    cv::GaussianBlur(float_gray, blur, Size(0,0), BLUR_FLT_NUMERATOR, BLUR_FLT_NUMERATOR);
    num = float_gray - blur;

    // denominator = sqrt(gauss_blur(img^2))
    cv::GaussianBlur(num.mul(num), blur, Size(0,0), BLUR_FLT_DENOMINATOR, BLUR_FLT_DENOMINATOR);
    cv::pow(blur, 0.5, den);

    // output = numerator / denominator
    gray = num / den;

    // normalize output into [0,1]
    cv::normalize(gray, gray, 0.0, 1.0, NORM_MINMAX, -1);

    // Display
    //namedWindow("demo", CV_WINDOW_AUTOSIZE );
    gray.convertTo(store_gray, CV_8U, 255);
    //imshow("demo", gray);

}

//attach_weight_2_mat(ptr_M_matrix, i, visual_all_feature, sqr_of_H_nod_plus1, Hidden_nodes, Height, Width);
void attach_weight_2_mat(float* ptr_M_matrix, int i, Mat src, int sqr_of_H_nod_plus1, int  Hidden_nodes, int Height, int Width)
{
    float *start_corner_offset = src.ptr<float>(0);
    int start_offset=0;
    float *src_zero_ptr = src.ptr<float>(0);
    float *src_ptr = src.ptr<float>(0);
    for(int j=0; j<Hidden_nodes ; j++)
    {

        start_offset = (j/sqr_of_H_nod_plus1)*Height*src.cols + (j%sqr_of_H_nod_plus1)*Width;
        start_corner_offset = start_offset + src_zero_ptr;
        src_ptr = start_corner_offset + (i/Width)*src.cols + (i%Width);
        *src_ptr = *ptr_M_matrix;
        ptr_M_matrix++;
    }
}
int main()
{

    float Rando=0.0f;
 //   float start_noise_range = 0.25f;//+/- start weight noise range
    float start_noise_range = 0.025f;//+/- start weight noise range

    printf("Auto encoder test program\n");
    //int get_data =0;
    int Height=0;
    int Width=0;
    int nr_of_pixels=0;
    int Hidden_nodes=0;
    int nr_of_pictures=0;
    int training_image=0;
    char answer_character;
    printf("Do you want default settings <Y>/<N> ? \n");
	answer_character = getchar();
	if(answer_character == 'Y' || answer_character == 'y')
	{
      //  Height = 48;
      //  Width = 64;
        Height = 28;
        Width = 28;

        Hidden_nodes = 40;
        nr_of_pictures = 80;
	}
	else
    {
    printf("Enter number of pixel Height of input pictures\n");
    scanf("%d", &Height);
    printf("Enter number of pixel Width of input pictures\n");
    scanf("%d", &Width);
    printf("Enter number of hidden nodes of input pictures\n");
    scanf("%d", &Hidden_nodes);
    printf("Enter number of hidden training pictures posXXX.JPG \n");
    scanf("%d", &nr_of_pictures);

    }
    nr_of_pixels = Height * Width;
    printf("Number of pixels = %d\n", nr_of_pixels);

	Mat src, image, mnist,m_gray;
///************** Raspicam **************
#ifdef USE_RASPICAM_INPUT
	raspicam::RaspiCam_Cv Camera;
	Camera.set( CV_CAP_PROP_FRAME_WIDTH, Width);
	Camera.set( CV_CAP_PROP_FRAME_HEIGHT, Height);
	Camera.set( CV_CAP_PROP_FORMAT, CV_8UC1 );
//Camera.set( CV_CAP_PROP_FORMAT, CV_8U );
	//Open camera
	cout<<"Opening Camera..."<<endl;
	if (!Camera.open()) {cerr<<"Error opening the camera"<<endl;return -1;}

	Camera.grab();
	Camera.retrieve (src);
	waitKey(1);
	src.convertTo(image, CV_32F, 1.0/255.0);//Convert pixels from 0..255 char to float 0..1

#endif // USE_RASPICAM_INPUT
///***************************************

#ifndef USE_RASPICAM_INPUT


///************Open mnist image ********

			sprintf(filename2, "mnist_480_360.jpg");//Assigne a filename "pos" with index number added
			sprintf(filename2, "mnist.png");//Assigne a filename "pos" with index number added

			mnist = imread( filename2, 1 );
			if ( !mnist.data )
			{
				printf("\n");
				printf("==================================================\n");
				printf("No image data Error! Probably not find mnist_480_360.jpg \n");
				printf("==================================================\n");
				printf("\n");
				//return -1;
			}


      cvtColor(mnist,m_gray,CV_BGR2GRAY);
      imshow("m_gray", m_gray);
        waitKey(1);

        int mnist_h1 = 28;
        int mnist_w1 = 28;
        int mnh=0;
        int mnw=0;
        int pos = 0;
        int ret;
   //     Mat m1;
for(int j=0;j<8;j++)
{
    for(int i=0;i<10;i++)
    {
//    Mat m1(image, Rect(0, 0, T0_FRAME_WIDTH, T0_FRAME_HEIGHT));// Rect(<start_x>, <start_y>, <width>, <hight>)
//48 x 45
        mnh = j*mnist_h1+j*1;
        mnw = i*mnist_w1+i*1;
        Mat m1(m_gray, Rect(mnw, mnh, mnist_w1, mnist_h1));// Rect(<start_x>, <start_y>, <width>, <hight>)
        cv::imwrite("temporary_file.JPG",m1);

            sprintf(filename_dst, "pos%d.JPG", pos);
            ret = rename("temporary_file.JPG", filename_dst);
            if(ret == 0)
            {
                printf("File renamed successfully");
            }
            else
            {
                printf("Error: unable to rename the file");
            }
        pos++;

    }
}

///*************************************

#endif // USE_RASPICAM_INPUT

   // mat_H =
    float *input_node;
    input_node = new float[nr_of_pixels];
    float *hidden_node;
    hidden_node = new float[Hidden_nodes];
    float *output_node;
    output_node = new float[nr_of_pixels];
    float **weight_matrix_M;//Pointer fo a dynamic array. This weight_matrix_M will then have a size of rows = nr_of_pixels, colums = Hidden_nodes.
    float **change_weight_M;//Pointer fo a dynamic array. This weight_matrix_M will then have a size of rows = nr_of_pixels, colums = Hidden_nodes.
    weight_matrix_M = new float *[nr_of_pixels];
    change_weight_M = new float *[nr_of_pixels];
    for(int i=0; i < nr_of_pixels; i++)
    {
        weight_matrix_M[i] = new float[Hidden_nodes];
        change_weight_M[i] = new float[Hidden_nodes];
    }

    printf("size of weight_matrix_M[2][0] =%d\n", sizeof weight_matrix_M[2][0]);

    int sqr_of_H_nod_plus1=0;
    sqr_of_H_nod_plus1 = sqrt(Hidden_nodes);
    sqr_of_H_nod_plus1 += 1;
    printf("sqr_of_H_nod_plus1 %d\n", sqr_of_H_nod_plus1);
    float* ptr_M_matrix;
    Mat visual_all_feature, outimg;
    visual_all_feature.create(Height * sqr_of_H_nod_plus1, Width * sqr_of_H_nod_plus1,CV_32F);
    outimg.create(Height, Width, CV_32F);

    srand (static_cast <unsigned> (time(0)));//Seed the randomizer
for(int i=0;i<nr_of_pixels;i++)
{
    ptr_M_matrix = &weight_matrix_M[i][0];
    for(int j=0;j<(Hidden_nodes);j++)
    {
        Rando = (float) (rand() % 65535) / 65536;//0..1.0 range
        Rando -= 0.5f;
        Rando *= start_noise_range;
        *ptr_M_matrix = Rando;
        ptr_M_matrix++;
        change_weight_M[i][j] = 0.0f;
    }
}

    for(int i=0;i<nr_of_pixels;i++)
    {
        ptr_M_matrix = &weight_matrix_M[i][0];
        attach_weight_2_mat(ptr_M_matrix, i, visual_all_feature, sqr_of_H_nod_plus1, Hidden_nodes, Height, Width);
    }
 //   local_normalizing(visual_all_feature);

//test on


        visual_all_feature += 0.5f;
    Mat color_img, gray;
     srand (static_cast <unsigned> (time(0)));//Seed the randomizer



   float error=0.0;
   float *zero_ptr_gray = gray.ptr<float>(0);
   float *ptr_gray = gray.ptr<float>(0);
   ptr_gray = zero_ptr_gray + (gray.cols * (gray.rows/2)) + gray.cols/2;
   float test_pixel_value = *ptr_gray;
   int *noise_pixels;
   noise_pixels = new int[nr_of_pixels];
   int noise_p_counter=0;

   float noise_ratio=0.0f;
   int nr_of_noise_rand_ittr=0;
   int rand_pix_pos=0;
    char keyboard;
    int started = 1;

    while(1)
    {
#ifdef USE_RASPICAM_INPUT
   	Camera.grab();
	Camera.retrieve (src);
	waitKey(1);
	src.convertTo(image, CV_32F, 1.0/255.0);//Convert pixels from 0..255 char to float 0..1
    imshow("image", image);
    gray = image;//Connect raspberry camera instead of posXXX.jpg image

#else
        training_image = rand() % nr_of_pictures;
     //   printf("training_image %d\n", training_image);
			sprintf(filename, "pos%d.JPG", training_image);//Assigne a filename "pos" with index number added
			color_img = imread( filename, 1 );
			if ( !color_img.data )
			{
				printf("\n");
				printf("==================================================\n");
				printf("No image data Error! Probably not find pos%d.JPG \n", training_image);
				printf("==================================================\n");
				printf("\n");
				//return -1;
			}

      cvtColor(color_img,gray,CV_BGR2GRAY);
      imshow("color_img", color_img);
      gray.convertTo(gray, CV_32F, 1.0/255.0);//Convert pixels from 0..255 char to float 0..1

#endif // USE_RASPICAM_INPUT
      //  local_normalizing(gray);
      sigmoid_mat(gray);
      zero_ptr_gray = gray.ptr<float>(0);
      ptr_gray = gray.ptr<float>(0);

///******************************************************
///************ Select noise pixels positions ***********
///******************************************************
    noise_p_counter=0;
    noise_ratio=0.0f;
    nr_of_noise_rand_ittr=0;
    rand_pix_pos=0;

    for(int n=0;n<nr_of_pixels;n++)
    {
        noise_pixels[n] = 0;
    }

    while(noise_ratio < (noise_percent*0.01f))
    {
        rand_pix_pos = (int) (rand() % nr_of_pixels);
            if(noise_pixels[rand_pix_pos] == 0)
            {
                noise_p_counter++;
            }
            noise_pixels[rand_pix_pos] = 1;
        noise_ratio = ((float)noise_p_counter) / ((float)nr_of_pixels);
        nr_of_noise_rand_ittr++;
        if(nr_of_noise_rand_ittr > 2*nr_of_pixels)
        {
            printf("give up fill random up noise this turn\n");
            printf("noise_ratio %f\n", noise_ratio);
            break;
        }
    }

///****************************************************
///**************** Feed forward **********************
///****************************************************
    ///Connect image pixel and also insert noise to input_node[]
    for(int n=0;n<nr_of_pixels;n++)
    {
        if(noise_pixels[n] == 1 && started == 1)
        {
            input_node[n] = 0.5f;///Insert noise 0.5f instead of real pixel value
        }
        else
        {
            ptr_gray = zero_ptr_gray + n;
            input_node[n] = *ptr_gray;///Insert pixel value
        }
    }
    ///*********** Forward to hidden nodes ****************
    for(int j=0;j<Hidden_nodes;j++)
    {
        hidden_node[j] = 0;
        for(int i=0;i<nr_of_pixels;i++)
        {
            hidden_node[j] += input_node[i] * weight_matrix_M[i][j];///Sum up values from input gained with weight
        }
        ///*** Sigmoid this node ***
        hidden_node[j] = 1.0/(1.0 + exp(-(hidden_node[j])));///Sigmoid function
    }
    ///********** Forward to output nodes *******************
    for(int i=0;i<nr_of_pixels;i++)
    {
        output_node[i] = 0.0f;
        for(int j=0;j<Hidden_nodes;j++)
        {
            output_node[i] += hidden_node[j] * weight_matrix_M[i][j];///Sum up values from hidden gained with weight
        }
        output_node[i] = 1.0/(1.0 + exp(-(output_node[i])));///Sigmoid function
    }

    float input_pixel =0.0f;
///****************************************************
///************* Calculate total loss *****************
///****************************************************
    /// k = nr_of_pixel
    /// loss = -SUM k (output[k] * log(input[k]) + (1.0 - output[k]) * log(1.0 - input[k]))
    /// or
    /// loss = 1/2 SUM k (input[k] - ouput[k])²
    float loss=0.0f;
    loss=0.0f;
    for(int i=0;i<nr_of_pixels;i++)
    {
        ptr_gray = zero_ptr_gray + i;///pick real input pixel
        input_pixel = *ptr_gray;///Insert pixel value
        loss += (input_pixel - output_node[i]) * (input_pixel - output_node[i]);/// loss = 1/2 SUM k (input[k] - ouput[k])²
    }
    static int print_loss=0;
    if(print_loss<10)
    {
        print_loss++;
    }
    else
    {
        printf("loss error = %f\n", loss);
       print_loss=0;
    }

///****************************************************
///**************** Backpropagation *******************
///****************************************************

    /// **** make Delta value for backpropagation and loss calculation ****
    float delta_pixel=0.0f;
    for(int i=0;i<nr_of_pixels;i++)
    {
        ptr_gray = zero_ptr_gray + i;///pick real input pixel
        input_pixel = *ptr_gray;///Insert pixel value
        delta_pixel = input_pixel - output_node[i] ;/// detal calculus

        /// **** update tied weight regarding delta
        for(int j=0;j<Hidden_nodes;j++)
        {
            if(started == 1)
            {
                change_weight_M[i][j] = LearningRate * hidden_node[j] * delta_pixel + Momentum * change_weight_M[i][j];
                weight_matrix_M[i][j] += change_weight_M[i][j];

            }
        }
    }

      float *zero_ptr_outimg = outimg.ptr<float>(0);
      float *ptr_outimg = outimg.ptr<float>(0);

      for(int i=0;i<nr_of_pixels;i++)
      {
          ptr_outimg = zero_ptr_outimg + i;
          *ptr_outimg = output_node[i];
      }

      gray -= 0.5;
      outimg -= 0.5;
      visual_all_feature -= 0.5;
      gray *= 2;
      outimg *= 2;
      visual_all_feature *= 2;
      gray += 0.5;
      outimg += 0.5;
      visual_all_feature += 0.5;


    for(int i=0;i<nr_of_pixels;i++)
    {
        ptr_M_matrix = &weight_matrix_M[i][0];
        attach_weight_2_mat(ptr_M_matrix, i, visual_all_feature, sqr_of_H_nod_plus1, Hidden_nodes, Height, Width);
    }


        visual_all_feature += 0.5f;

      imshow("visual_all_feature", visual_all_feature);

      imshow("gray", gray);

        imshow("outimg", outimg);
        waitKey(1);

///******** start stop
		if(kbhit()){
            keyboard = getchar();
            if(keyboard== ' ')
            {
				if(started == 1)
				{
					started = 0;
					printf("Stop training\n");
				}
				else
				{
					started = 1;
					printf("Start training\n");
				}

			}

        }

///***************
    if(started == 0)
    {
        waitKey(2000);
    }

    }
    return 0;
}